1. Triple Pattern Fragments
Ruben Taelman - @rubensworks
imec - Ghent University
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2. Evaluate SPARQL queries client-side with TPF
SELECT ?person ?city WHERE {
?person rdf:type dbpedia-owl:Artist.
?person dbpedia-owl:birthPlace ?city.
?city foaf:name "Waterloo"@en.
}
?person rdf:type dbpedia-owl:Artist.
?person dbpedia-owl:birthPlace ?city.
?city foaf:name "Waterloo"@en. 2

3. Publishing with Triple Pattern Fragments
Server
Client
Evaluations
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4. Publishing with Triple Pattern Fragments
Server
Client
Evaluations
4

5. Simple triple pattern interface
Triple pattern queries, paged results
Example: s1 p1 o1 at page 2
http://example.org/my-dataset
?subject=s1&predicate=p1&object=o1&page=2
Metadata and controls
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6. Effective caching because of limited number of URI’s
Frequently used TPF’s are cached
Cached TPF’s can be delivered efficiently
Queries with common data fragments will evaluate faster
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7. Storage solution must support triple pattern queries
In-memory triplestore for RDF files
HDT (Fernández 2010)
SPARQL endpoints
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8. Server is simple
In order to make publication cheap and easy
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9. Publishing with Triple Pattern Fragments
Server
Client
Evaluations
9

10. Clients can evaluate any SPARQL query
using the simple TPF interface of one or more servers
Split up SPARQL queries into separate triple pattern queries
Combine results client-side
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11. Joining triple pattern fragments can be tricky
SELECT ?person ?city WHERE {
?person rdf:type dbpedia-owl:Artist.
?person dbpedia-owl:birthPlace ?city.
?city foaf:name "Waterloo"@en.
}
?person rdf:type dbpedia-owl:Artist.
?person dbpedia-owl:birthPlace ?city.
?city foaf:name "Waterloo"@en.
Some query plans are more efficient than others
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12. Number of triples as metadata in TPF’s
?person rdf:type dbpedia-owl:Artist.
?person dbpedia-owl:birthPlace ?city.
?city foaf:name "Waterloo"@en. 26
96 000
12 000 000
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13. Select most selective triple pattern
?person rdf:type dbpedia-owl:Artist.
?person dbpedia-owl:birthPlace ?city.
?city foaf:name "Waterloo"@en. 26
96 000
12 000 000
Most selective!
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14. Find all results for most selective pattern
?person rdf:type dbpedia-owl:Artist.
?person dbpedia-owl:birthPlace ?city.
?city foaf:name "Waterloo"@en.
dbp:Waterloo,_Iowa foaf:name "Waterloo"@en
dbp:Waterloo,_London foaf:name "Waterloo"@en
dbp:Waterloo,_Ontario foaf:name "Waterloo"@en
...
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15. Fill in results in other patterns
?person rdf:type dbpedia-owl:Artist.
?person dbpedia-owl:birthPlace dbp:Waterloo,_Iowa.
?city foaf:name "Waterloo"@en.
dbp:Waterloo,_Iowa foaf:name "Waterloo"@en
dbp:Waterloo,_London foaf:name "Waterloo"@en
dbp:Waterloo,_Ontario foaf:name "Waterloo"@en
...
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16. Recursively repeat for remaining patterns
?person rdf:type dbpedia-owl:Artist.
?person dbpedia-owl:birthPlace dbp:Waterloo,_Iowa .
96 000
45
16

17. Select most selective pattern
?person rdf:type dbpedia-owl:Artist.
?person dbpedia-owl:birthPlace dbp:Waterloo,_Iowa .
96 000
45
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18. Find all results for most selective pattern
?person rdf:type dbpedia-owl:Artist.
?person dbpedia-owl:birthPlace dbp:Waterloo,_Iowa .
96 000
45
dbp:Allan_Carpenter dbo:birthPlace dbp:Waterloo,_Iowa.
dbp:Adam_DeVine dbo:birthPlace dbp:Waterloo,_Iowa.
dbp:Bonnie_Koloc dbo:birthPlace dbp:Waterloo,_Iowa.
...
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19. Fill in results in other patterns
dbp:Allan_Carpenter rdf:type dbpedia-owl:Artist.
?person dbpedia-owl:birthPlace dbp:Waterloo,_Iowa .
96 000
45
dbp:Allan_Carpenter dbo:birthPlace dbp:Waterloo,_Iowa.
dbp:Adam_DeVine dbo:birthPlace dbp:Waterloo,_Iowa.
dbp:Bonnie_Koloc dbo:birthPlace dbp:Waterloo,_Iowa.
...
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20. One solution is found
dbp:Allan_Carpenter rdf:type dbpedia-owl:Artist. 1
Repeat process for all other matches
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21. Joining algorithm is not always optimal
Other algorithms are possible
Improved algorithm minimizes #requests (Van Herwegen 2015)
Additional metadata may improve query plans
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22. Publishing with Triple Pattern Fragments
Server
Client
Evaluations
22

23. Send many client queries to a single server
1 server (TPF, Virtuoso, Fuseki)
1 - 244 simultaneous clients
Different query types from Berlin SPARQL benchmark
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(Verborgh 2016)

24. Query throughput is lower
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25. Server load is lower
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26. TPF is just one possible trade-off
Reduce client load:
Additional metadata for membership search (Vander Sande 2015)
Substring filtering (Van Herwegen 2015)
Dynamic data publication and querying (Taelman 2016)
Reduce server load:
Decentralized caching (Folz 2016)
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27. Conclusions
TPF servers have a simple low-cost interface
TPF clients evaluate SPARQL queries locally, using this
interface
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28. Next session
Setting up a TPF server yourself
Querying the server
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29. Sources
R Verborgh “Linked Data Publishing”
http://rubenverborgh.github.io/WebFundamentals/linked-data-publishing/
R. Verborgh, M. Vander Sande, O. Hartig, et al. Triple Pattern Fragments: a Low-cost
Knowledge Graph Interface for the Web.
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